1. Field of Art
The present invention generally relates to the field of sales and advertising, and more specifically, to ways of determining optimal bid amounts for term advertising in an online context.
2. Description of the Related Art
In an online advertising system, advertisers contract with an advertising publisher, such as search engines such as GOOGLE, YAHOO!, or MSN, to run their advertisements. Typically, an advertiser specifies a term, e.g. a keyword or phrase, that is relevant to its advertisements, an amount of money (“bid”) that the advertiser is willing to pay if the advertising publisher selects its advertisements for display in association with the term rather than those of another advertiser, and a user clicks on the displayed advertisement or otherwise designates it for further viewing. Bids are generally calculated by the advertiser based on the expected probability of the occurrence of a further desired action (“conversion event”) taking place. The conversion event can be, for example, the purchase of a product associated with the advertisement. In response to a user request for information that is associated with the term, the advertising publisher selects an advertisement to display from among the advertisements of all advertisers submitting a bid for that term.
Conversion rates (i.e., the probabilities that a conversion event specified by the advertiser will take place, given that the advertisement is clicked or otherwise designated for viewing), or information such as number of advertisement clicks and the subsequent number of conversions from which conversion rates may be derived, are typically tracked by advertisers. The conversion rates for popular terms (i.e. terms that receive many clicks) are statistically reliable. However, for less-popular terms having little information (“low volume” terms), whatever information there is may be of dubious statistical significance. For example, a term corresponding to a broad product category, e.g. “scooters,” would likely have a large amount of information available, e.g. that advertisements about scooters were clicked on 9,352 times, and that conversion actions further took place 877 times, for a conversion rate of approximately 9 percent. In contrast, a term corresponding to a particular product part number within that category, e.g. “RX-1955,” might have very little information available, such as that advertisements relating to it were clicked on 2 times, and that conversion events took place 1 time, for a 50 percent conversion rate. However, it is doubtful that the high 50 percent conversion rate is truly representative of the term and would remain at that high rate as the amount of information on the term grows over time. Because any information about low-volume terms is unlikely to be statistically significant, conventional systems for recommending bids for a low-volume term ignore the known conversion rate for that term, instead merely computing a conversion rate that is an average of conversion rates for terms with a statistically significant amount of information. This may result in imprecise bid calculations for low-volume terms. Given the large number of low-volume terms relative to popular, high-volume terms, it would be beneficial to more accurately compute conversion rates for low-volume terms and thus be able to calculate more accurate term bids.